We strongly believe that an experimentation-focused approach is the key to sustain a successful marketing organization. Marketing teams that master testing and learning can outlearn their competition by delivering not only better products, but also more relevant ads and experiences, and ultimately drive growth. Transforming your team to have an experimental mindset and building a culture of experimentation is the first step.
If you are interested in more details about building a culture of experimentation, check out this article:
How to Establish a Data-Driven Culture of Experimentation and Supercharge Growth
Always-on experimentation is an agile process that enables companies to iterate, optimize, and innovate faster by continually learning from ever-running marketing experiments. Implementing always-on experimentation can have a tremendous impact on how businesses conduct their marketing practices and drive growth.
Learn more about how agile marketing teams leverage always-on experimentation in this article:
How Winning Teams Leverage Always-on Experimentation
No, even if you currently do not run marketing experiments, you can still leverage Quantify to make better data-driven decisions.
Quantify will help you to understand the credibility of your performance data and enable you to make better decisions faster, while eliminating guesswork and the dependency on data scientists.
With all the possibilities that Quantify provides, there are still common-sense limits. For instance, it might not make sense to compare click-through rates of large advertisements with those of text links. While Quantify will deliver results in this case, the usefulness of these results will be limited.
Learn in this article why you should move away from A/B significance testing:
7 Reasons to Move Away From A/B Significance Testing
Quantify uses Bayesian statistics, which is in many ways superior to other approaches. Quantify models probability distributions for all metrics and calculates credible intervals. A credible interval specifies the range that includes 95% of all probable values. Quantify also provides credible intervals for actual lifts, so it can tell you how much better a variation performs in comparison to other variations. These tests can be run both as on-off tests or as continuous tests, even while changing the test’s underlying variations during the process.
As soon as the results are credible enough, Quantify will automatically draw meaningful conclusions and highlight winners and losers.
We explain the the basics of Bayesian statistics in this video: Watch Video
If you are interested in the mathematical theory and methodology behind our Bayesian statistics engine, please check-out our whitepaper: Download Whitepaper
We continuously learn about new use-cases for Quantify as you can leverage it to analyze any test or outcome that is somewhat measurable. Here are a couple of examples:
User Acquisition, Dynamic Creative Optimization, RTB Optimization, Campaign Optimisation, Channel Exploration, Business Model Validation, Incrementality Testing, Onsite Experiments, Innovation Testing, Conversion/Funnel Optimization, Product/Feature Validation, Promotion/Coupon/Price Testing, CRM/Email Optimization, Survey Analytics, Audience Optimization, POS Optimization, Direct Mail Testing
We can model every conceivable metric in Quantify. Quantify supports the following metric types:
The free version of Quantify, QuantifyNow, features a limited set of metrics for each data source. Please reach out via the feedback form if you are missing a metric.
QuantifyNow provides a growing number of integrations with selected media partners. If your data source is not available at this time, you can upload your data via an Excel or CSV file (only conversion rates, though).
We are currently working on adding new data sources. If you are missing a data source, please let us know your priorities via this survey.
You might also consider upgrading to the full version of Quantify that provides the ability to integrate any data source and metric.